Measuring Greenland Ice Sheet Melt Using Spaceborne GNSS Reflectometry From TechDemoSat‐1

Li, Weiqiang; Cardellach, Estel; Fabra, Fran; Ribó, S.; Rius, A.

doi:10.1029/2019gl086477

Cited by 19 publications

(16 citation statements)

References 50 publications

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“…The exploitation of this approach has been particularly accelerated with the availability of large and diverse datasets acquired from recent spaceborne GNSS-R observatories such as the United Kingdom's TechDemoSat-1 (TDS-1, launched in mid-2014 and retired in mid-2019) [12] and NASA's Cyclone Global Navigation Satellite System (CYGNSS, launched in late 2016) [13]. While TDS-1's GNSS-R measurements featured a relatively low revisit time due to the satellites intention for evaluating multiple payloads simultaneously, the dedicated research community surrounding TDS-1 has greatly improved the literature's understanding of spaceborne GNSS-R responses to ocean winds [14,15], ice sheets [16,17], and land geophysical parameter features [18][19][20] from space due to TDS-1's global coverage created by its polar orbiting configuration. On the other hand, CYGNSS features a high-temporal resolution over a smaller spatial extent in order to optimize its measurements for ocean studies.…”

Section: Introductionmentioning

confidence: 99%

Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations

et al. 2020

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This paper presents a machine learning (ML) framework to derive a quasi-global soil moisture (SM) product by direct use of the Cyclone Global Navigation Satellite System (CYGNSS)’s high spatio-temporal resolution observations over the tropics (within ±38 latitudes) at L-band. The learning model is trained by using in-situ SM data from the International Soil Moisture Network (ISMN) sites and various space-borne ancillary data. The approach produces daily SM retrievals that are gridded to 3 km and 9 km within the CYGNSS spatial coverage. The performance of the model is independently evaluated at various temporal scales (daily, 3-day, weekly, and monthly) against Soil Moisture Active Passive (SMAP) mission’s enhanced SM products at a resolution of 9 km × 9 km. The mean unbiased root-mean-square difference (ubRMSD) between concurrent (same calendar day) CYGNSS and SMAP SM retrievals for about three years (from 2017 to 2019) is 0.044 cm3 cm−3 with a correlation coefficient of 0.66 over SMAP recommended grids. The performance gradually improves with temporal averaging and degrades over regions regularly flagged by SMAP such as dense forest, high topography, and coastlines. Furthermore, CYGNSS and SMAP retrievals are evaluated against 170 ISMN in-situ observations that result in mean unbiased root-mean-square errors (ubRMSE) of 0.055 cm3 cm−3 and 0.054 cm3 cm−3, respectively, and a higher correlation coefficient with CYGNSS retrievals. It is important to note that the proposed approach is trained over limited in-situ observations and is independent of SMAP observations in its training. The retrieval performance indicates current applicability and future growth potential of GNSS-R-based, directly measured spaceborne SM products that can provide improved spatio-temporal resolution than currently available datasets.

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Section: Introductionmentioning

confidence: 99%

Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations

et al. 2020

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“…In this context, over the last 15 years, we have witnessed the arrival of GNSS-R (Global Navigation Satellite System Reflectometry) technology, which initially demonstrated its considerable potential for the characterization of ocean surface properties (waves, etc.) [13][14][15][16][17]. It was then tested over continental surfaces, where it revealed its strong potential for the monitoring of various surface parameters such as the water content of the soil or vegetation biomass [18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Desert Roughness Retrieval Using CYGNSS GNSS-R Data

et al. 2020

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The aim of this paper is to assess the potential use of data recorded by the Global Navigation Satellite System Reflectometry (GNSS-R) Cyclone Global Navigation Satellite System (CYGNSS) constellation to characterize desert surface roughness. The study is applied over the Sahara, the largest non-polar desert in the world. This is based on a spatio-temporal analysis of variations in Cyclone Global Navigation Satellite System (CYGNSS) data, expressed as changes in reflectivity (Γ). In general, the reflectivity of each type of land surface (reliefs, dunes, etc.) encountered at the studied site is found to have a high temporal stability. A grid of CYGNSS Γ measurements has been developed, at the relatively fine resolution of 0.03° × 0.03°, and the resulting map of average reflectivity, computed over a 2.5-year period, illustrates the potential of CYGNSS data for the characterization of the main types of desert land surface (dunes, reliefs, etc.). A discussion of the relationship between aerodynamic or geometric roughness and CYGNSS reflectivity is proposed. A high correlation is observed between these roughness parameters and reflectivity. The behaviors of the GNSS-R reflectivity and the Advanced Land Observing Satellite-2 (ALOS-2) Synthetic Aperture Radar (SAR) backscattering coefficient are compared and found to be strongly correlated. An aerodynamic roughness (Z0) map of the Sahara is proposed, using four distinct classes of terrain roughness.

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“…Furthermore, as TDS-1 was in a polar orbit, it was able to show the potential of cryospheric applications, e.g. [10][11][12]. Ocean altimetry is a tantalizing target but achieving coherent carrier phase precision over a rough surface remains a technical challenge achieved only under certain conditions [29].…”

Section: Gnss Reflectometry Backgroundmentioning

confidence: 99%

An Introduction to the HydroGNSS GNSS Reflectometry Remote Sensing Mission

Unwin

Pierdicca

Cardellach

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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HydroGNSS has been selected as the second ESA Scout Earth Observation mission to demonstrate the capability of small satellites to deliver science. This paper summarises the case for HydroGNSS, as developed during its System Consolidation study. HydroGNSS is a high value dual small satellite mission, which will prove new concepts and offer timely climate observations that supplement and complement existing observations and are high in ESA's Earth Observation scientific priorities. The mission delivers observations of four hydrological Essential Climate Variables (ECVs) as defined by the Global Climate Observing System (GCOS) using the new technique of GNSS Reflectometry. These will cover the world's land mass to 25 km resolution, with a 15 day revisit. The variables are soil moisture, inundation or wetlands, freeze / thaw state and above ground biomass.

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Measuring Greenland Ice Sheet Melt Using Spaceborne GNSS Reflectometry From TechDemoSat‐1

Cited by 19 publications

References 50 publications

Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations

Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations

Desert Roughness Retrieval Using CYGNSS GNSS-R Data

An Introduction to the HydroGNSS GNSS Reflectometry Remote Sensing Mission

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